def test_concept_drift_stream(test_path):
stream = ConceptDriftStream(random_state=1, position=20, width=5)
stream.prepare_for_use()
assert stream.n_remaining_samples() == -1
expected_names = [
"salary", "commission", "age", "elevel", "car", "zipcode", "hvalue",
"hyears", "loan"
]
assert stream.feature_names == expected_names
expected_targets = [0, 1]
assert stream.target_values == expected_targets
assert stream.target_names == ['target']
assert stream.n_features == 9
assert stream.n_cat_features == 3
assert stream.n_num_features == 6
assert stream.n_targets == 1
assert stream.get_info() == 'ConceptDriftStream: ' \
'First Stream: AGRAWALGenerator - ' \
'Drift Stream: AGRAWALGenerator - ' \
'alpha: 0.0 - position: 20 - width: 5'
assert stream.has_more_samples() is True
assert stream.is_restartable() is True
# Load test data corresponding to first 10 instances
test_file = os.path.join(test_path, 'concept_drift_stream.npz')
data = np.load(test_file)
X_expected = data['X']
y_expected = data['y']
X, y = stream.next_sample()
assert np.alltrue(X[0] == X_expected[0])
assert np.alltrue(y[0] == y_expected[0])
X, y = stream.last_sample()
assert np.alltrue(X[0] == X_expected[0])
assert np.alltrue(y[0] == y_expected[0])
stream.restart()
X, y = stream.next_sample(30)
assert np.alltrue(X == X_expected)
assert np.alltrue(y == y_expected)
assert stream.n_targets == np.array(y).ndim
assert stream.n_features == X.shape[1]
assert 'stream' == stream.get_class_type()
def test_concept_drift_stream(test_path):
stream = ConceptDriftStream(random_state=1, position=20, width=5)
assert stream.n_remaining_samples() == -1
expected_names = ["salary", "commission", "age", "elevel", "car", "zipcode", "hvalue", "hyears", "loan"]
assert stream.feature_names == expected_names
expected_targets = [0, 1]
assert stream.target_values == expected_targets
assert stream.target_names == ['target']
assert stream.n_features == 9
assert stream.n_cat_features == 3
assert stream.n_num_features == 6
assert stream.n_targets == 1
assert stream.has_more_samples() is True
assert stream.is_restartable() is True
# Load test data corresponding to first 10 instances
test_file = os.path.join(test_path, 'concept_drift_stream.npz')
data = np.load(test_file)
X_expected = data['X']
y_expected = data['y']
X, y = stream.next_sample()
assert np.alltrue(X[0] == X_expected[0])
assert np.alltrue(y[0] == y_expected[0])
X, y = stream.last_sample()
assert np.alltrue(X[0] == X_expected[0])
assert np.alltrue(y[0] == y_expected[0])
stream.restart()
X, y = stream.next_sample(30)
assert np.alltrue(X == X_expected)
assert np.alltrue(y == y_expected)
assert stream.n_targets == np.array(y).ndim
assert stream.n_features == X.shape[1]
assert 'stream' == stream._estimator_type
expected_info = "ConceptDriftStream(alpha=0.0,\n" \
" drift_stream=AGRAWALGenerator(balance_classes=False,\n" \
" classification_function=2,\n" \
" perturbation=0.0,\n" \
" random_state=112),\n" \
" position=20, random_state=1,\n" \
" stream=AGRAWALGenerator(balance_classes=False,\n" \
" classification_function=0,\n" \
" perturbation=0.0, random_state=112),\n" \
" width=5)"
assert stream.get_info() == expected_info